Collaborating Authors


The Top 100 Software Companies of 2021


The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...

Steven Fleischman on LinkedIn: Industry Standard AI infrastructure powered by HPE, VMware, and NVIDIA


AI technology used to be limited to advanced research teams. It is now a key capability for many businesses to improve sales and product quality, provide deep personalisation and new interfaces, and to improve safety and reduce risk. AI is materially changing how we interact with and benefit from technology. Having ready access to and consistent operational control over AI infrastructure is a game changer that democratizes AI for enterprises and opens access to many new use cases.

VMware's vSphere software now certified to run Nvidia AI workloads


VMware on Tuesday announced updates to its server virtualization software vSphere, including the latest progress in its partnership with Nvidia aimed at accelerating AI adoption. The company also announced updates to its storage virtualization portfolio vSAN 7 and said that both of the new releases will support enterprises with developer- and AI-ready infrastructure. On the server virtualization front, VMware announced that Nvidia AI workloads are certified to run on vSphere 7. The new Nvidia software suite, officially named Nvidia AI Enterprise, aims to give enterprises the ability to develop AI software for a range of applications such as advanced diagnostics in healthcare, smart factories for manufacturing, and fraud detection in financial services. According to VMware and Nvidia, the software suite provides multi-node, AI application performance on vSphere that is indistinguishable from bare-metal servers. Currently, vSphere 7 is the only virtualization platform certified to support the Nvidia AI software suite.

VMware CEO on preparing for a pandemic no one knew was coming


VMware's VMworld 2020 was, like many other annual tech events, forced to shift online this year. But with a slew of announcements made during the event centred on the future of work and succeeding in this new paradigm, it was almost like VMware was ready for a rapid shift in its customer's operations. "My job every day is position VMware so that we're better positioned for the strategic future, where technologies are going, and clearly this year, as we would say, for an unpredictable world," VMware CEO Pat Gelsinger said. Speaking with ZDNet, Gelsinger said his company "flipped" to remote working over a weekend and "hasn't looked back". The lessons the company learned internally were then translated to helping customers.

VMware, Nvidia partner to make AI chips easier for businesses to use


VMware makes software that helps businesses get more work out of data center servers by slicing physical machines into "virtual" ones so that more applications can be packed onto each physical machine. Its tools are commonly used by large businesses that operate their own data centers as well as businesses that use cloud computing data centers. For many years, much of VMware's work focused on making software work better with processors from Intel Corp, which had a dominant market share of data centers. In recent years, as businesses have turned to AI for everything from speech recognition to recognizing patterns in financial data, Nvidia's market share in data centers has been expanding because its chips are used to speed up such work. VMware's software tools will work smoothly with Nvidia's chips to run AI applications without "any kind of specialized setup," Krish Prasad, head of VMware's cloud platform business unit, said during a press briefing.

VMware, Nvidia integrate on architecture, technology as they aim to accelerate AI adoption


VMware and Nvidia are integrating the latter's artificial intelligence applications for unified management of apps, security and data processing unit accelerators. The partnership secures Nvidia's role in hybrid clouds as VMware outlined an architecture that incorporates data processing units (DPUs) in the data center, cloud and edge. Specifically, AI software on Nvidia's NGC hub will be integrated into VMware vSphere, Cloud Foundation and Tanzu. Both companies said the bet was that the integration would be able to speed up AI adoption in enterprises. Krish Prasad, general manager of VMware's cloud platform business, said "AI workloads no longer need any kind of specialized set up based on bare metal or specialized tools to run it."

The History, Status, and Future of FPGAs

Communications of the ACM

This article is a summary of a three-hour discussion at Stanford University in September 2019 among the authors. It has been written with combined experiences at and with organizations such as Zilog, Altera, Xilinx, Achronix, Intel, IBM, Stanford, MIT, Berkeley, University of Wisconsin, the Technion, Fairchild, Bell Labs, Bigstream, Google, DIGITAL (DEC), SUN, Nokia, SRI, Hitachi, Silicom, Maxeler Technologies, VMware, Xerox PARC, Cisco, and many others. These organizations are not responsible for the content, but may have inspired the authors in some ways, to arrive at the colorful ride through FPGA space described here. Field-programmable gate arrays (FPGAs) have been hitting a nerve in the ASIC community since their inception. In the mid-1980s, Ross Freeman and his colleagues bought the technology from Zilog and started Xilinx, targeting the ASIC emulation and education markets.

My seven favorite Windows 10 features


A few weeks ago, I shared my list of Seven Windows 10 annoyances (and how to fix them). It was one of my most popular posts of the year, and so for a sequel I decided to offer the flip side, listing my seven favorite features of Windows 10. As I was putting this list together, one thing that struck me is how many of these features were either missing or poorly implemented when Windows 10 debuted in 2015. But all of the features I have chosen to spotlight here are well tested, well implemented, and guaranteed to make you more productive. Are you looking for Windows 10 Home or Windows 10 Pro?

Dell Technologies rolls out systems for HPC, AI workloads leveraging VMware's Bitfusion


The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Dell Technologies is rolling out a series of designs and systems that aim to speed up artificial intelligence deployments by using VMware's acquired Bitfusion technology. Two Dell EMC Ready Solutions are based on VMware Validated Designs to combine Dell EMC hardware with VMware Cloud Foundation and AI management Bitfusion tools in VMware vSphere 7. Dell Technologies said that its Dell Dell Technologies is claiming to be among the first IT companies to equip systems to run AI workloads within VMware environments. Ravi Pendekanti, senior vice president of product management and marketing for Dell Technologies server unit, said the new systems are designed to run AI anywhere and take advantage of underutilized GPUs. "GPU instances are being underutilized and that is holding back AI," said Pendekanti.

Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud Machine Learning

FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100 s overhead. Such designs lead to poor isolation and heavy performance loss for multiple users, which are far away from providing efficient and flexible FPGA virtualization for neither public nor private cloud scenarios. To solve these problems, we introduce a novel virtualization framework for instruction architecture set (ISA) based on DNN accelerators by sharing a single FPGA. We enable the isolation by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, further leading to performance isolation for multiple users. On the other hand, to overcome the heavy re-compilation overheads, we propose a tiling-based instruction frame package design and two-stage static-dynamic compilation. Only the light-weight runtime information is re-compiled with $\sim$1 ms overhead, thus the performance is guaranteed for the private cloud. Our extensive experimental results show that the proposed virtualization design achieves 1.07-1.69x and 1.88-3.12x throughput improvement over previous static designs using the single-core and the multi-core architectures, respectively.